SWB-DM: A Calibrated Sliced-Wasserstein-Barycenter Aggregator with Delayed-Momentum Caching for Byzantine-Robust Federated Learning under Partial Participation

arXiv:2609.16099 · cs.LG, cs.CR, cs.DC · Submitted 2026-09-14 · Read on arXiv

cs.LG, cs.CR, cs.DC

Submitted: 2026-09-14

Updated: 2026-09-29

Comments: 7 pages, 2 figures, 5 tables

Code: https://github.com/saranrajsnkr/SWB-DM-full-empirical-study

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 23× in this paper · explained in 1 episode
• Aggregation — 22× in this paper · explained in 1 episode
• Federated Learning — 8× in this paper · explained in 15 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Optimal Transport — 3× in this paper · explained in 5 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes

Sources

• Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation

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